Instructions to use LiquidAI/LFM2.5-1.2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-1.2B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-1.2B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LiquidAI/LFM2.5-1.2B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-1.2B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-1.2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct

SGLang

How to use LiquidAI/LFM2.5-1.2B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/LFM2.5-1.2B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-1.2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/LFM2.5-1.2B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-1.2B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-1.2B-Instruct with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct
```

Trouble with Data Extraction using Custom Schema

by Purplys - opened Jan 20

Discussion

Purplys

Jan 20

•

edited Jan 20

Hello Liquid AI team,

I've tried to use both this model and LFM2-1.2B-Extract for data extraction of texts.
However, I find that these models are very prone to errors and hallucinations when I specify a custom schema. As an example,

system:

Identify and extract information matching the following schema. Return data as a JSON object. Missing data should be omitted.

Schema:
- discovery: "what was being discovered/invented"
- person: "the person who made the discovery"
- where: "the location where the discovery was made"
- when: "the time when the discovery was made"
- doi: "doi of the source"

user input:

The lightning rod was invented by Benjamin Franklin in the early 1750s.

model response:

{
  "discovery": "lightning rod",
  "person": "Benjamin Franklin",
  "where": "early 1750s",
  "when": "early 1750s",
  "doi": "10.1007/978-3-319-25588-9"
}

Clearly, "early 1750s" is not a location, and the model hallucinated a doi when the original text contained none.
If I removed the custom schema from the system prompt, the model is correct a lot more often. However, this results in inconsistent JSON schemas that might include missing/extraneous information which are hard to ingest.
Temperature is set to 0. I'm not sure if I'm doing something wrong? I would love to use your very speedy and lightweight models otherwise, but currently the error rates make them a pass.

mlabonne

Liquid AI org Jan 26

Hey @Purplys , sorry for the late reply. I tried reproducing what you described, but I get pretty good results with your example (see this chat with the playground). Can you double-check your generation parameters?

Purplys

Feb 3

Hey @Purplys , sorry for the late reply. I tried reproducing what you described, but I get pretty good results with your example (see this chat with the playground). Can you double-check your generation parameters?

Hi, thanks for your response!

I double checked my generation parameters again. Turns out I had repetition_penalty too high. Toning it down to your recommended settings largely fixed the issues.

By the way, if I may ask, do you have any plans on releasing finetunes of LFM2.5 for extraction? Kind of like the Liquid Nano series for LFM2.

mlabonne

Liquid AI org Feb 4

Great! This is something we're exploring at the moment, so that's good feedback. Thanks!

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